Abstract
Web advertising services for the Internet are growing rapidly. However, it is difficult to effectively recommend Web advertisements to latent buyers using the current mainstream Web ad recommendation method based on keyword matching with online search and browser history. This is because it is an explicit analysis using keywords that the user is already interested in. On the other hand, most of the advertisement recommendation methods using location information of mobile terminals are based on the distance to the actual store. Therefore, this research proposes and verifies a method to analyse latent user interest based on analysis of real-space user behavior considering point-of-interest (POI) attributes. We apply the method to Web advertisement recommendation on mobile terminals, and assume that the user’s interest targets are stores that exist in real space. Specifically, we use geotagged tweet data to extract the users’ movement activity range within a certain time period with reference to the store locations. The movement activity range serves as basis for POI attribute analysis using OpenStreetMap (OSM) data. Finally, the characteristics of the real-space movement activity history are extracted and used together with the POI attributes to train a XGBoost model. In previous research, we accumulated data, generated training models with various settings, and verified their prediction accuracy of users visiting a target stores. In this paper, we focus on verifying the effectiveness of the proposed advertising recommendation method by conducting a questionnaire survey of actual users.
Published Version
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